In [1]:
import matplotlib.pyplot as plt
import numpy as np
import librosa
import librosa.display
import IPython.display as ipd
Loading Audio Files¶
In [2]:
debussy_file = "audio/debussy.wav"
redhot_file = "audio/redhot.wav"
duke_file = "audio/duke.wav"
In [3]:
ipd.Audio(debussy_file)
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In [4]:
ipd.Audio(redhot_file)
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In [5]:
ipd.Audio(duke_file)
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In [6]:
# load audio files with librosa
debussy, sr = librosa.load(debussy_file)
redhot, _ = librosa.load(redhot_file)
duke, _ = librosa.load(duke_file)
Root-mean-squared energy with Librosa¶
In [7]:
FRAME_SIZE = 1024
HOP_LENGTH = 512
In [8]:
rms_debussy = librosa.feature.rms(debussy, frame_length=FRAME_SIZE, hop_length=HOP_LENGTH)[0]
rms_redhot = librosa.feature.rms(redhot, frame_length=FRAME_SIZE, hop_length=HOP_LENGTH)[0]
rms_duke = librosa.feature.rms(duke, frame_length=FRAME_SIZE, hop_length=HOP_LENGTH)[0]
Visualise RMSE + waveform¶
In [9]:
frames = range(len(rms_debussy))
t = librosa.frames_to_time(frames, hop_length=HOP_LENGTH)
In [10]:
# rms energy is graphed in red
plt.figure(figsize=(15, 17))
ax = plt.subplot(3, 1, 1)
librosa.display.waveplot(debussy, alpha=0.5)
plt.plot(t, rms_debussy, color="r")
plt.ylim((-1, 1))
plt.title("Debusy")
plt.subplot(3, 1, 2)
librosa.display.waveplot(redhot, alpha=0.5)
plt.plot(t, rms_redhot, color="r")
plt.ylim((-1, 1))
plt.title("RHCP")
plt.subplot(3, 1, 3)
librosa.display.waveplot(duke, alpha=0.5)
plt.plot(t, rms_duke, color="r")
plt.ylim((-1, 1))
plt.title("Duke Ellington")
plt.show()
RMSE from scratch¶
In [11]:
def rmse(signal, frame_size, hop_length):
rmse = []
# calculate rmse for each frame
for i in range(0, len(signal), hop_length):
rmse_current_frame = np.sqrt(sum(signal[i:i+frame_size]**2) / frame_size)
rmse.append(rmse_current_frame)
return np.array(rmse)
In [12]:
rms_debussy1 = rmse(debussy, FRAME_SIZE, HOP_LENGTH)
rms_redhot1 = rmse(redhot, FRAME_SIZE, HOP_LENGTH)
rms_duke1 = rmse(duke, FRAME_SIZE, HOP_LENGTH)
In [13]:
plt.figure(figsize=(15, 17))
ax = plt.subplot(3, 1, 1)
librosa.display.waveplot(debussy, alpha=0.5)
plt.plot(t, rms_debussy, color="r")
plt.plot(t, rms_debussy1, color="y")
plt.ylim((-1, 1))
plt.title("Debusy")
plt.subplot(3, 1, 2)
librosa.display.waveplot(redhot, alpha=0.5)
plt.plot(t, rms_redhot, color="r")
plt.plot(t, rms_redhot1, color="y")
plt.ylim((-1, 1))
plt.title("RHCP")
plt.subplot(3, 1, 3)
librosa.display.waveplot(duke, alpha=0.5)
plt.plot(t, rms_duke, color="r")
plt.plot(t, rms_duke1, color="y")
plt.ylim((-1, 1))
plt.title("Duke Ellington")
plt.show()
Zero-crossing rate with Librosa¶
In [14]:
zcr_debussy = librosa.feature.zero_crossing_rate(debussy, frame_length=FRAME_SIZE, hop_length=HOP_LENGTH)[0]
zcr_redhot = librosa.feature.zero_crossing_rate(redhot, frame_length=FRAME_SIZE, hop_length=HOP_LENGTH)[0]
zcr_duke = librosa.feature.zero_crossing_rate(duke, frame_length=FRAME_SIZE, hop_length=HOP_LENGTH)[0]
In [15]:
zcr_debussy.size
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Visualise zero-crossing rate with Librosa¶
In [16]:
plt.figure(figsize=(15, 10))
plt.plot(t, zcr_debussy, color="y")
plt.plot(t, zcr_redhot, color="r")
plt.plot(t, zcr_duke, color="b")
plt.ylim(0, 1)
plt.show()
ZCR: Voice vs Noise¶
In [17]:
voice_file = "audio/voice.wav"
noise_file = "audio/noise.wav"
In [18]:
ipd.Audio(voice_file)
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ipd.Audio(noise_file)
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